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| Section | Objectives |
|---|---|
| Implement Natural Language Processing Solutions | - Text analytics and summarization - Translation and multilingual support - Language understanding and intent recognition |
| Implement Computer Vision Solutions | - Image classification and object detection - OCR and document intelligence |
| Develop Generative AI Applications and Agents | - AI agents architecture
|
| Plan and Manage Azure AI Solutions | - Azure AI resource provisioning and configuration - Model selection and lifecycle management - Responsible AI principles and governance |
| Knowledge Mining and Information Retrieval | - RAG (Retrieval Augmented Generation) patterns - Azure AI Search configuration - Indexing and semantic search |
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NEW QUESTION # 35
You are developing prompts for a Micosoft Foundry project that classifies incoming support tickets by category.
You need to improve accuracy by showing the model how correct classifications look, without retaining the model or storing knowledge permanently.
Which prompt engineering approach should you use?
Answer: D
Explanation:
Few-shot prompting is the best approach for this project. This technique improves classification accuracy by including a few high-quality examples directly inside the prompt, giving the model a clear pattern to follow without modifying its weights or storing data permanently.
Zero retraining: Works entirely through in-context learning during the API call.
No permanent storage: The knowledge disappears as soon as the inference request is completed.
Immediate accuracy boost: Demonstrates formatting, nuances, and edge cases directly to the model.
Reference:
https://www.linkedin.com/pulse/top-interview-questions-answers-prompt-engineering-nitin-sharma-ka4fc
NEW QUESTION # 36
A multimodal agent processes user-uploaded images. An attacker embeds hidden text instructions inside an image, hoping the agent will read and act on them. Which Azure AI Content Safety capability is designed to detect this kind of attack?
Answer: A
Explanation:
Prompt Shields detects prompt injection attacks, including indirect injection where malicious instructions are embedded in content the model processes, such as text hidden inside an image.
NEW QUESTION # 37
You need to recommend an invoice review solution that resolves the issue reported by the finance department.
What should you include in the recommendation?
Answer: C
Explanation:
The correct recommendation is Azure Content Understanding in Foundry Tools . The case study states that Contoso's finance department must manually review vendor invoices to verify that invoice details match vendor contract terms, and that the invoices contain tables, logos, and varied layouts that make consistent processing difficult. It also states that the planned solution must evaluate both the visual layout and textual content of the invoices.
Azure Content Understanding is designed for this type of multimodal document-processing workload.
Microsoft describes Content Understanding as a Foundry Tool that processes unstructured and multimodal content, including documents and images, and transforms it into structured output for AI applications. It can use document analyzers to extract text, layout, tables, fields, and relationships from diverse document types.
Chat completions alone would not reliably extract structured invoice fields from complex layouts. Azure Document Intelligence can extract OCR, layout, and tables, but Content Understanding is the better end-to- end Foundry capability for combining visual and textual understanding with structured extraction for downstream verification. Image Analysis focuses on image-level visual features and is insufficient for invoice field and table review. Reference topics: Content Understanding, document analyzers, multimodal extraction, invoice processing, tables, layout, and structured JSON output.
NEW QUESTION # 38
You are planning a Microsoft Foundry project named Project1 that will contain multiple agents. Each agent will access the same Azure Al Search resource.
You need to recommend a solution to centrally manage the Azure Al Search credentials within Project1. The solution must be implemented across all the agents.
What should you recommend?
Answer: B
NEW QUESTION # 39
You have an application named App1 that uses Azure Speech in Foundry Tools to transcribe live calls.
Transcript segments often contain both English and Spanish. App1 sends each segment to Azure Translator in Foundry Tools to translate to another language.
Sometimes, mixed-language segments result in incomplete or incorrect translations.
You need to reduce translation errors. The solution must ensure that the entire transcript is translated successfully.
What should you do before sending the segments to Translator?
Answer: A
Explanation:
The correct answer is C. Split the mixed-language segments into single-language segments and translate each segment separately . Azure Translator has a known limitation for text that contains mixed-language input within the same sentence or segment. Microsoft's Translator known-issues guidance states that the Text translation API does not support translating sentences that contain mixed-language text and that translations can be incorrect or incomplete when a single sentence includes multiple languages. The documented workaround is to specify the intended source language, remove the mixed-language sentence, or split the text into single-language segments.
In this scenario, specifying English for all segments would fail when Spanish content is present. Automatic language detection can detect a dominant language, but it does not reliably solve a segment that contains both English and Spanish. Document translation is also not the right fix because the issue is mixed-language content inside transcript segments, and Microsoft notes similar incomplete results for mixed-source-language documents. Splitting the transcript into language-homogeneous segments ensures each translation request has a clear source language and lets the entire transcript be translated reliably. Reference topics: Azure Translator in Foundry Tools, text translation, language detection, mixed-language input, and transcript preprocessing.
NEW QUESTION # 40
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